随机切削模型的修正

Pub Date : 2023-08-07 DOI:10.1017/apr.2023.22
Fabian Burghart
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引用次数: 0

摘要

我们提出了一种对图的随机破坏的改进:给定一个有限网络,具有一组不同的源和目标,随机去除(切割)顶点,丢弃不包含源节点的组件。我们研究了移除所有目标所需的切割次数,以及剩余图形的大小。该模型在Meir和Moon (J. Austral)的随机切割模型之间进行插值。数学。Soc. 11, 1970)和场地渗透。我们证明了几个一般结果,包括剩余图的大小是一个紧的随机变量族的相容序列的扩展型图,并确定了二叉毛虫树和完全二叉树的极限分布。
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A modification of the random cutting model
Abstract We propose a modification to the random destruction of graphs: given a finite network with a distinguished set of sources and targets, remove (cut) vertices at random, discarding components that do not contain a source node. We investigate the number of cuts required until all targets are removed, and the size of the remaining graph. This model interpolates between the random cutting model going back to Meir and Moon ( J. Austral. Math. Soc. 11 , 1970) and site percolation. We prove several general results, including that the size of the remaining graph is a tight family of random variables for compatible sequences of expander-type graphs, and determine limiting distributions for binary caterpillar trees and complete binary trees.
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